Applications, or ‘apps’, supporting the agronomic decisions and data management associated with production agriculture often require the delineation of fields as an initial step in setup of accounts associated with a particular farming entity. This process can be accomplished in any number of ways, but methodologies that are universally practical across the whole of the Earth are quite limited.
In some countries, such as the United States and countries within the European Union, governments have created GIS datasets of field boundaries, often as part of the administration of government programs supporting production agriculture. Though public access to these GIS datasets is sometimes limited, where they are made available they can substantially ease the process of field identification and associated boundary delineation. One significant problem with the use of such datasets, however, is that they require ongoing maintenance in order to be kept up-to-date with changing field boundaries. Field boundaries can change for any number of reasons, including changes of ownership, drainage activities, ‘new breaking’ of previously untilled ground or the return of farmed ground to pasture or native vegetation, human development, etc. This requires an ongoing commitment on the part of entities maintaining such GIS datasets in order to keep them current, and thus relevant.
Another option is to use GPS technology onboard agricultural equipment to acquire field boundary data. GPS-enabled autonomous equipment operation and data collection are becoming relatively ubiquitous in some of the more developed regions of the world. Where available, this data can provide highly accurate field boundary information that can be imported into software applications in order to ease the process of field setup, though the difficulties of exporting and importing these data between disparate software systems can be problematic. One drawback of this approach to field boundary setup is that boundary data is not often available for newly-acquired land, so that data must be acquired and then exported/imported as new fields are added, as well as when any changes are made to existing boundaries (which, as discussed earlier, can change quite regularly for any number of reasons).
In the absence of GIS datasets, the most common approach to field setup in agricultural applications is to provide users with georeferenced aerial imagery of their fields displayed in an interactive mapping engine, and then provide users with drawing tools by which they can draw the boundaries of fields by tracing out features evident in the aerial imagery or other reference imagery that is provided. However, the manual nature of this up-front process, in combination with the ever-increasing size of farms globally, can be a substantial deterrent to onboarding and acceptance of potentially valuable applications.
Given these issues, a new approach to automating or aiding field boundary detection, identification, and maintenance would be welcomed within the global agricultural industry. Perhaps the most promising approaches are based on the idea that field boundary information can be automatically detected from aerial imagery of farmland. Such imagery has become ubiquitous with the surge in satellite-based ‘Earth sensing’ platforms in recent years. These satellite-based systems can provide multi-spectral imagery of locations across the entirety of the Earth with revisit intervals presently measured in days to weeks. The impending explosion in the use of micro-satellites and remotely-piloted vehicles (commonly referred to as drones) for the collection of imagery and other remotely-sensed data will only serve to make imagery-based approaches to field boundary detection more appealing going forward.
Successful algorithm development to-date has nonetheless been limited by a number of real-world problems that make field boundaries difficult to identify. In-field variability in crop health owing to any number of causes, many of which may be transient, can create apparent (but false) boundaries within a single field. Further, the same crops are often growing in neighboring fields, at similar growth stages, and with similar spectral signatures that are not easily differentiable in remotely-sensed data. This can make it impossible to detect where one field ends and the next begins.